A predictive model (also known as a forecaster, a forecasting model, a prediction model, or an autoregressive model) is a software-implemented model of a system, process, or phenomenon, usable to forecast—or predict—a value, output, or outcome expected from the system, process, or phenomenon. The system, process, or phenomenon that is modeled is collectively and interchangeably referred to hereinafter as a “process” unless specifically distinguished where used.
A simulation is a method of computationally looking ahead in the future of the execution of the process to predict one or more events that can be expected to occur in the process at that future time. A predicted event is a value, output, or outcome of the process at the end of a look-ahead period configured in the simulation.
A variable value that affects an outcome of a process is called a factor or a feature. A predicted event or an outcome of a process—to wit, a prediction—is dependent upon, affected by, or otherwise influenced by a set of one or more factors. A factor can be independent of and not affected by other factors participating in a given model. An independent factor is also called an independent variable. A factor can be dependent upon a combination of one or more other independent or dependent factors. A dependent factor is also called a dependent variable.
A predictor is a factor, and can be an independent variable or a dependent variable. Under certain circumstances, dependent variable may not act as a predictor in a model but as a predicted outcome of the model.
A predictive model has to be trained before the model can reliably predict an event in the future of the process with a specified degree of probability or confidence. Usually, but not necessarily, the training data includes past or historical outcomes of the process. The training process adjusts a set of one or more parameters of the model.
A predictive model can also self-train using a machine learning process. The predictive model selects some of its own prior outputs depending upon some combination of the validity, accuracy, repeatability, and reliability of those prior outputs. The predictive model then consumes the selected prior outputs as training inputs, to improve some combination of the validity, accuracy, repeatability, and reliability of future outputs. For example, a predictive model compares a prediction from a prior output with an actual outcome of the event, which is also referred to as a ground truth. The self training seeks to improve the accuracy of the model by attempting to reduce a difference between the prediction and the ground truth in future predictions.
Time series forecasting uses one or more forecasting models to regress on independent variables to produce a dependent variable. For example, if Tiger Woods has been playing golf very quickly, the speed of play is an example of an independent variable. A forecasting model regresses on historical data to predict the future play rates. The future play rate is a dependent variable.
Generally, a predictive model can be used to predict any type of event. For example, a model can be configured to predict a load on a server at a future time as a consequence of a number of tennis matches going on at the time, a number of social media messages being communicated at the time about the matches, and a number of web accesses by users while the matches are being played. The load is the predicted event, where the prediction of the load has been modeled to have some relationship with the number of matches, the number of messages, and the number of web accesses, each of those numbers being an independent variable or a predictor that forms an input to the model.